85 tagged with "Machine Learning"
Machine learning techniques for financial data analysis and automation
LLMs Are Not Useful for Time Series Forecasting: What NeurIPS 2024 Means for Finance AI
A NeurIPS 2024 Spotlight paper ablates three LLM-based time series forecasting methods — OneFitsAll, Time-LLM, and CALF — and finds that removing the language model improves accuracy in most cases, with up to a 1,383× training speedup. For finance AI applications like Beancount balance prediction, lightweight purpose-built models consistently beat repurposed LLMs.
AuditCopilot: LLMs for Fraud Detection in Double-Entry Bookkeeping
AuditCopilot applies open-source LLMs (Mistral-8B, Gemma, Llama-3.1) to corporate journal entry fraud detection, cutting false positives from 942 to 12 — but ablation reveals the LLM functions primarily as a synthesis layer on top of Isolation Forest scores, not as an independent anomaly detector.
TAT-LLM: Ge-fined-tunde LLaMA 2 voor discreet redeneren over financiële tabellen en tekst
TAT-LLM fine-tunt LLaMA 2 7B met LoRA op financiële tabel-tekst QA-benchmarks en behaalt 64,60% EM op FinQA — waarmee het de 63,91% van GPT-4 verslaat — door redenering te ontleden in deterministische Extraheer-Redeneer-Voer-uit stappen die rekenkundige fouten elimineren.
Fine-Tuning vs. RAG: Why Retrieval Wins for Injecting New Knowledge into LLMs
Empirical comparison of RAG vs. unsupervised fine-tuning across 7B-parameter LLMs shows RAG achieves 0.875+ accuracy on post-cutoff facts while fine-tuning plateaus at 0.504 — with direct implications for Beancount agent design and any system requiring frequent knowledge updates.
IRCoT: Interleaving Retrieval with Chain-of-Thought for Multi-Step QA
IRCoT interleaves BM25 retrieval with each step of a chain-of-thought reasoning loop, achieving +11.3 retrieval recall and +7.1 F1 on HotpotQA over one-step RAG — and shows a 3B model can beat GPT-3 175B when retrieval strategy is right.
FLARE: Active Retrieval Augmented Generation
FLARE (EMNLP 2023) improves on standard RAG by triggering retrieval mid-generation using token-probability confidence thresholds, reaching 51.0 EM on 2WikiMultihopQA versus 39.4 for single-retrieval — but calibration failures in instruction-tuned chat models limit its reliability for production finance agents.
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Lewis et al.'s NeurIPS 2020 paper introduced the hybrid RAG architecture—a BART-large generator paired with a FAISS-indexed retriever over 21 million Wikipedia passages—achieving 44.5 EM on Natural Questions and establishing the parametric/non-parametric split that now underlies most production AI systems. This review covers RAG-Sequence vs. RAG-Token trade-offs, the retrieval collapse failure mode, and what stale indexes mean for financial AI built on append-only Beancount ledgers.
MultiHiertt: Benchmarking Numerical Reasoning Over Multi-Hierarchical Financial Tables
MultiHiertt (ACL 2022) introduces 10,440 QA pairs from real financial reports averaging 3.89 hierarchical tables each; state-of-the-art models score 38% F1 versus 87% for humans, with a 15-point penalty for cross-table questions — quantifying the retrieval gap finance AI must close.
ConvFinQA: Multi-Turn Financial QA and the 21-Point Gap Between Models and Human Experts
ConvFinQA (EMNLP 2022) extends FinQA into multi-turn conversation over S&P 500 earnings reports, finding that the best fine-tuned model achieves 68.9% execution accuracy versus 89.4% for human experts—and drops to 52.4% on hybrid multi-aspect conversations where models must carry numerical context across different financial topics.
TAT-QA: Hybrid Table-Text QA Benchmark for Financial Annual Report Reasoning
TAT-QA is a 16,552-question benchmark over hybrid table-plus-text financial report contexts that showed evidence grounding — not arithmetic — is the core bottleneck in finance AI; by 2024, fine-tuned 7B LLMs reached 83% F1, closing most of the gap against a 91% human ceiling.
FinQA: The Benchmark Measuring AI Numerical Reasoning on Financial Reports
FinQA (EMNLP 2021) built 8,281 QA pairs from S&P 500 earnings reports requiring multi-step arithmetic programs. Neural models scored 61% at release versus 91% for human experts; accuracy collapses to 22% on three-or-more-step programs. The failure modes — domain constants, cross-modality grounding, chain length — map directly to the challenges Beancount agents face today.
FinanceBench: Why Vector-Store RAG Fails on Real Financial Documents
FinanceBench evaluates 16 AI configurations against 10,231 questions from real SEC filings; shared-vector-store RAG answers correctly only 19% of the time, and even GPT-4-Turbo with the oracle passage reaches just 85% accuracy — showing that numerical reasoning, not retrieval, is the binding constraint for enterprise finance AI.